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Compound regressive models for quantitative multivariate phenotypes: application to lipid and lipoprotein data

P Bagchi¹, O Jiang, G E Bonney

¹Fox Chase Cancer Center, Philadelphia, Pennsylvania.

Genetic Epidemiology

|January 1, 1993

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This study introduces compound class D regressive models for analyzing multiple phenotypic outcomes. The findings are demonstrated using Donner Laboratory data, simplifying genetic analysis by not assuming a major gene.

Area of Science:

Statistics
Genetics
Biostatistics

Background:

Multivariate phenotypic data analysis presents statistical challenges.
Class D regressive models offer a framework for analyzing complex trait data.
Understanding genetic influences on phenotypes requires robust statistical modeling.

Purpose of the Study:

To extend class D regressive models to a compound version for multivariate phenotypic outcomes.
To present the likelihood function for these compound models.
To illustrate the application of the models using real-world genetic data.

Main Methods:

Development of the compound class D regressive model for p-variate phenotypic data.
Derivation and presentation of the model's likelihood function.

Related Experiment Videos

Application of the model to the Donner Laboratory dataset.

Main Results:

The compound class D regressive model effectively analyzes multivariate phenotypic outcomes.
The likelihood function provides a basis for parameter estimation and inference.
Illustrative analysis on Donner Laboratory data showcases model utility without major gene assumptions.

Conclusions:

Compound class D regressive models offer a flexible approach to multivariate genetic analysis.
The presented methodology facilitates the study of complex phenotypic traits.
The Donner Laboratory data example highlights the practical applicability of the models.